2021 Census Reveals a Broken Glass Ceiling: A Transformative Shift

1 Introduction

In the exploration of this data set, a unique narrative emerges as we investigate the relationship between gender and income. A narrative that unfolds a trend that challenges conventional norms. The proverbial “Glass Ceiling”, an invisible barrier that hinders and acts as an obstacle in the way of success for women, was found to be shattered (Inam et al. 2020). For, there exists evidence that females, irrespective of ethnicity have outperformed their male counterparts.

This provided dataset consists of a total of 9 variables with prominent ones being Age, Income, and Highest Education. Our focus will be based on income, gender, highest education, and ethnicity. Additionally, color pallets for visualization have been selected to facilitate color blind individuals.

Data has been manipulated with help of tidyverse such as changing column names, sectioning and subdividing columns(Wickham et al. 2019). Gggplot2 and viridis packages have been used in conjunction to provide access for color blind individuals (Wickham 2016). Furthermore, it has been convenient to use our dataset with help of reader library(Wickham, Hester, and Bryan 2023).

2 Exploration and Comparison: A Level Playing Field

This section is dedicated to the visual analysis of how key factors such as education, age, and marital status are distributed across genders. Gender has been modified and realized to produce the graph and filtered by age group with the help of ggplot. This has been done so that the working-age population (18-55) can be identified. Initially, we learn that the number of females is not greater than males by a significant magnitude as illustrated in Figure 1. Therefore, there is a low chance of bias on the basis of numerical strength.

A higher education level often correlates to a higher income (wu2021a?). Furthermore, income is expected to rise with age (araghi2019?). Therefore, age and education are visualized in Figure 2 and Figure 3 respectively.

Code

df <- df %>% rename(HED = `Highest Ed`) # Renaming the 'Highest Ed' column to 'HED'
df_filtered <- df %>%
  filter(!is.na(HED), !is.na(Female)) %>%
  mutate(
    Gender = ifelse(Female == 0, "Female", "Male"),  # Renaming and creating subgroups for age
    Age_Group = case_when(
      Age >= 18 & Age <= 35 ~ "18-35",
      Age >= 36 & Age <= 55 ~ "36-55",
      Age > 55 ~ "55+",
      TRUE ~ NA_character_
    )
  ) %>%
  group_by(Age_Group, Gender) %>%
  summarise(count = n()) %>%
  filter(!is.na(Age_Group))  # Filtering out NA rows in Age_Group

p <- df_filtered %>% # Creating a ggplot object and adding count labels on bars
  ggplot(aes(x = Age_Group, y = count, fill = Gender, label = count)) +  # Using 'Gender' column and adding count label
  geom_bar(stat = "identity", position = "stack", alpha = 0.7) +  # Creating stacked bar plot
  geom_text(position = position_stack(vjust = 0.5), size = 3) +  # Adding count labels
  labs(
    title = "Figure 1: Distribution of Females and Males by Age Group",  # Title and axis labels
    x = "Age Group",
    y = "Count",
    fill = "Gender"
  ) +
  scale_fill_viridis_d(option = "plasma") +  # Setting color palette
  scale_fill_viridis_d(option = "viridis", begin = 0.2, end = 0.8) +  # Color-blind friendly
  theme_minimal()  # Applying minimal theme

ggplotly(p) # Converting ggplot object to an interactive plot using Plotly

Figure 2 illustrates a density plot for genders divided on the basis of Ethnicity created with the assistance of ggplot and geom_density (Wickham 2016). It tells us that the magnitude of females is marginally more than the number of males.

Code

df %>%
 mutate(Gender = ifelse(Female == 0, "Female", "Male"), # Adding a 'Gender' column based on 'Female' values
         Eth = factor(Eth)) %>%  # Converting Eth column to factor for categorical display
  ggplot(aes(x = Age, fill = Gender)) +  # Mapping Age to x-axis and Gender to fill color
  geom_density(alpha = 0.7) +  # Creating density plot with transparency
  scale_fill_manual(values = c("#D55E00", "#56B4E9"), labels = c("Female", "Male")) +  # Setting color for gender
  labs(
    title = "Figure 2: Density Plot of Age by Gender",
    x = "Age", y = "Density", fill = "Gender"  # Labels for title, axes, and legend
  ) +facet_grid(~ Eth) +  # Faceting by ethnicity
  scale_fill_viridis(discrete = TRUE) +  # Color-blind friendly palette for ethnicity
  theme_minimal()  # Applying a minimal theme

Education level has been illustrated in Figure 3 which tells us that Females match their male counterparts for bachelor’s degrees and outperform them in terms of Masters level education.

Code

df %>%
  mutate(Gender = ifelse(Female == 0, "Female", "Male")) %>%  # Creating 'Gender' column based on 'Female' values
  filter(!is.na(HED), !is.na(Female)) %>%  # Removing rows with missing values in 'HED' and 'Female' columns
  ggplot(aes(x = factor(HED), fill = Gender)) +  # Mapping 'HED' to x-axis and 'Gender' to fill color
  geom_bar(position = "dodge", alpha = 0.7) +  # Creating a dodged bar plot with transparency
  geom_text(aes(label = after_stat(count)),  # Adding count labels on bars
            stat = "count", position = position_dodge(width = 0.9), size = 4, color = "black") + #custom labels
  labs(
    title = "Figure 3: Distribution of Females and Males by Education",  # Title and axis labels
    x = "Education Level", y = "Count", fill = "Gender"
  ) +
  scale_fill_manual(values = c("#D55E00", "#56B4E9"), labels = c("Female", "Male")) +  # Setting color labels
  theme_minimal() +  # Applying a minimal theme
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) -> my_plot  # Rotating x-axis labels
ggplotly(my_plot)  # Converting the ggplot object to an interactive plot using Plotly

3 The Interesting Thing: A Shattered Glass Ceiling

Having discovered that factors such as age and education were almost similar for males as well as females, it has been found that the median income for females is higher than the median income for males as suggested by Figure 4. The median income has been mentioned in Figure 4.

Furthermore, the rise in female income has been observed irrespective of communities as the average female income is higher when compared to their male counterparts.

Additionally, the lower end of female income is higher than the lower end of male across ethnicities with the exception of the Black ethnicity where the lower end income was almost similar.

Code

suppressWarnings({
  df %>%
    mutate(
      Gender = ifelse(Female == 0, "Female", "Male"),
      Eth = ifelse(is.na(Eth), "Unknown", as.character(Eth))  # Handling missing values in Eth column
    ) %>%
    select(-Female) %>%
    ggplot(aes(x = Eth, y = INC, fill = Gender)) +
    geom_boxplot(alpha = 0.7, outlier.shape = NA, width = 0.6, pattern = "O") +
    stat_summary(fun = median, geom = "text", aes(label = scales::comma(..y..)),
                 position = position_dodge(width = 0.75), vjust = -0.5, show.legend = FALSE) +  # Adding median labels
    scale_fill_manual(values = c("#56B4E9", "#E69F00"), labels = c("Female", "Male"), guide = "none") +  # Removing legend for fill
    labs(title = "Figure 4: Income Distribution by Ethnicity and Gender",
         x = "Ethnicity", y = "Income") +
    scale_y_continuous(labels = scales::comma, limits = c(0, 40000)) +
    theme_minimal()
})

Figure 3 has been generated using ggplot2 and dplyr libraries for generating a boxplot (cox2019?). It categorizes genders and ethnicities while handling missing values. Median values have been printed on the visualization for better understanding.

4 The Critique: Limitations and assumptions

The conclusion that females have higher incomes than males may assume direct comparisons without considering factors like job roles, industries, or working hours. For instance, due to a lack of information regarding working hours, it is not possible to further discern if there is any disparity between the genders. Furthermore, industries in which individuals are employed along with their designation would be beneficial to avoid any bias.

It has been found that the magnitude of White ethnicity is overrepresented in the dataset as suggested by Figure 5 has the potential to lead to biases and generalization of findings. Additionally, there are missing values present in the dataset, which may be a case of data mishandling.

The segmentation of age groups (18-35, 36-55, 55+), especially when there is a lack of information regarding the occupation or job of the individuals, might oversimplify the complex nature of labor force distributions. Additionally, another feature of the analysis could have been added such as identifying the role of women as Business owners or job creators.

Code

# Removing rows with missing income values
df <- df[!is.na(df$INC), ]

# Creating a bar plot for ethnicity with a color-blind friendly palette
ggplot(df, aes(x = Eth)) +
  geom_bar(fill = viridis_pal()(length(unique(df$Eth)))) + # Using viridis color palette
  geom_text(
    stat = "count", 
    aes(label = stat(count)), 
    vjust = -0.5, 
    size = 3
  ) + # Adding count labels to the bars
  labs(
    title = "Figure 5: Bar Plot of Ethnicity",
    x = "Ethnicity",
    y = "Count"
  )

5 Conclusion

The exploration of the dataset has shown us interesting trends that challenge contemporary gender norms with respect to income. Contrary to historical trends, it has been found that females irrespective of ethnicity earn more than their male counterparts on average. Furthermore, the lower-end earning of females is more than the lower-end of males with a single exception of the Black ethnicity.

While these findings suggest a transformative shift in gender dynamics, it’s imperative to acknowledge certain limitations. The lack of granularity, particularly regarding job roles, industries, and working hours, hinders a comprehensive understanding of income disparities. Additional insights into employment sectors and designations could provide a more nuanced perspective.

Moreover, the overrepresentation of certain ethnic groups in the dataset raises concerns about potential biases and generalization. Addressing missing values and ensuring a more balanced representation across ethnicities could enhance the robustness of future analyses.

References

Inam, Hina, Mahin Janjua, Russell S. Martins, Nida Zahid, Sadaf Khan, Abida K. Sattar, Aneela Darbar, et al. 2020. “Cultural Barriers for Women in Surgery: How Thick Is the Glass Ceiling? An Analysis from a Low Middle-Income Country.” World Journal of Surgery 44 (9): 2870–78. https://doi.org/10.1007/s00268-020-05544-9.

Wickham, Hadley. 2016. “Ggplot2: Elegant Graphics for Data Analysis.” https://ggplot2.tidyverse.org.

Wickham, Hadley, Mara Averick, Jennifer Bryan, Winston Chang, Lucy D’Agostino McGowan, Romain François, Garrett Grolemund, et al. 2019. “Welcome to the Tidyverse” 4: 1686. https://doi.org/10.21105/joss.01686.

Wickham, Hadley, Jim Hester, and Jennifer Bryan. 2023. “Readr: Read Rectangular Text Data.” https://CRAN.R-project.org/package=readr.